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fc4585c 77ef07a fc4585c 77ef07a fc4585c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 | import numpy as np
class MultiAttractorSystem:
"""
MUCE‑Ω v2 Core Engine
"""
def __init__(self, dim=2, K=3):
self.dim = dim
self.K = K
self.Xi = np.random.randn(dim)
self.history = []
self.w = np.ones(K) / K
self.lr = 0.05
self.beta = 0.2
self.gamma = 0.1
self.max_norm = 5.0
def potentials(self, Xi):
return np.array([
np.linalg.norm(Xi),
-np.linalg.norm(Xi - 2.0),
np.linalg.norm(Xi + 1.0),
])
def grad_potentials(self, Xi):
eps = 1e-6
return np.array([
Xi / (np.linalg.norm(Xi) + eps),
-(Xi - 2.0) / (np.linalg.norm(Xi - 2.0) + eps),
(Xi + 1.0) / (np.linalg.norm(Xi + 1.0) + eps),
])
def project_constraints(self, Xi):
norm = np.linalg.norm(Xi)
if norm > self.max_norm:
return Xi / norm * self.max_norm
return Xi
def utilities(self):
stability = -np.linalg.norm(self.Xi)
exploitation = np.linalg.norm(self.Xi)
oscillation = np.var(self.history[-10:], axis=0).mean() if len(self.history) > 10 else 0.0
return np.array([stability, exploitation, -oscillation])
def step(self):
grads = self.grad_potentials(self.Xi)
total_grad = np.sum(self.w[:, None] * grads, axis=0)
if len(self.history) > 5:
osc = np.var(self.history[-5:], axis=0)
else:
osc = 0.0
self.Xi -= self.lr * (total_grad + self.gamma * osc)
self.Xi = self.project_constraints(self.Xi)
self.history.append(self.Xi.copy())
self.w += self.beta * self.utilities()
self.w = np.exp(self.w) / np.sum(np.exp(self.w))
return self.Xi.copy(), self.w.copy() |